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ISPRS Int. J. Geo-Inf. 2014, 3(2), 619-637; doi:10.3390/ijgi3020619

Geo-Based Statistical Models for Vulnerability Prediction of Highway Network Segments

1
Department of Geography and Environmental Studies, University of Haifa, Mt. Carmel, Haifa 39105, Israel
2
Division of Transportation and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel
*
Author to whom correspondence should be addressed.
Received: 30 December 2013 / Revised: 8 April 2014 / Accepted: 14 April 2014 / Published: 29 April 2014
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Abstract

This study describes four statistical models—Poisson; Negative Binomial; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial—which were devised in order to examine traffic accidents and estimate the best probability estimating model in terms of future risk assessment at interurban road sections. The study was conducted on four sets of fixed-length sections of the road network: 500, 750, 1000, and 1500 m. The contribution of transportation and spatial parameters as predictors of road accident rates was evaluated for all four data sets separately. In addition, the Empirical Bayes method was applied. This method uses historical accidents information, allowing regression to the mean phenomenon so as to improve model results. The study was performed using Geographic Information System (GIS) software. Other analyses, such as statistical analyses combined with spatial parameters, interactions, and examination of other geographical areas, were also performed. The results showed that the short road sections data sets of 500 and 750 m yielded the most stable models. This allows focused treatment on short sections of the road network as a way to save resources (enforcement; education and information; finance) and potentially gain maximum benefit at minimum investment. It was found that the significant parameters affecting accident rates are: curvature of the road section; the region and traffic volume. An interaction between the region and traffic volume was also found. View Full-Text
Keywords: GIS; traffic accidents; probability models; transportation; highway; spatial; Poisson; negative binomial; Zero-Inflated; Empirical Bayes GIS; traffic accidents; probability models; transportation; highway; spatial; Poisson; negative binomial; Zero-Inflated; Empirical Bayes
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Pollak, K.; Peled, A.; Hakkert, S. Geo-Based Statistical Models for Vulnerability Prediction of Highway Network Segments. ISPRS Int. J. Geo-Inf. 2014, 3, 619-637.

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